A GIS-based multimedia watershed model: development and application

A multimedia model was developed using publicly available geographical information system (GIS) data, chemical release information and local monitoring networks to assess the fate of trichloroethene (TCE) within the Passaic River Watershed. Seven environmental media, air, water, sediment, surface soil, terrestrial vegetation, root zone soil and vadose zone soil, were modeled in this study along with their sub-compartments. The Passaic River Watershed is described using the NJDEP geographical information system (GIS) resources, the United States Geological Survey (USGS) and the United States Soil Conservation Services (US SCS) soil data. The introduction of spatial resolution to a multimedia, unsteady state model is performed in this work, and represents an important step in expanding the use of equilibrium models to provide far reaching information on the fate of toxic contaminants within a given environmental unit. The spatial representation of cross-boundary fluxes was successfully demonstrated with the use of sub-watershed as an environmental unit and the direct assessment of TCE for each of the 11 sub-watersheds that make up the Passaic River Basin in northern New Jersey. Important data gaps identified during the development of this model include the lack of comprehensive monitoring data on organic contaminants, and non-uniformity among available physical environmental data from different government agencies.     

Assessing Spatial Scale Effects on Hydrometric Network Design Using Entropy and Multi‐objective Methods

In order to facilitate water resources decisions, it is important that accurate and informative hydrometric data are collected. Combining information theory with multi‐objective optimization has led to methods of optimizing the information content provided by hydrometric networks; however, there is no available study on the effects of spatial scale and data limitation on these methods. Herein, a dual entropy multi‐objective optimization (DEMO) and a transinformation (TI) analysis were done to recommend optimal locations for additional hydrometric stations in the Madawaska Watershed. This analysis was designed to be comparative to a similar study conducted on the Ottawa River Basin which encompasses the Madawaska Watershed to allow for an investigation of the spatial scale effects in this type of network design. This study concludes that TI analysis is not adversely affected by scaling; however, the DEMO analysis is sensitive to the placement of potential station locations and the size of the study area. This study also examines the benefit of including nearby stations when the area of interest does not have a sufficient number of existing hydrometric stations for analysis. It is shown that these stations can provide useful information because their inclusion in the analysis increased the average TI in the watershed. Recommendations were made as to the ideal locations of additional stations in the Madawaska Watershed hydrometric network.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

Development of performance indicators for small Quebec drinking water utilities

This study presents a comparative performance analysis of small drinking water utilities in Quebec (Canada). The investigation bears on 10 utilities that use surface water or groundwater under the direct influence of runoff and apply chlorination as the only treatment before distribution. The utilities under study were divided into two groups: four utilities that had never or rarely provided water violating provincial drinking water microbiological standards (relating to fecal and/or total coliform bacteria), called nonproblematic utilities, and six utilities that quite often violated the standards, designated as problematic utilities. The objective of the study is to develop utility performance indicators capable of explaining current and historical distributed water quality. Indicators are based on operational, infrastructure, and maintenance characteristics of utilities that are integrated using a multivariable weight-based index. Results show that utility performance indicators are systematically better for the nonproblematic group of utilities as compared to the problematic group. Disinfection-related, infrastructure, and maintenance variables are those that most contributed to indicator values. Sensitivity analyses served to assess the impact on indicator results of excluding variables and changing their weights.     

Impact of edible oil injection on the permeability of aquifer sands

Recent laboratory and field studies have shown that food-grade edible oils can be injected into the subsurface for installation of in-situ permeable reactive barriers. However to be effective, the oil must be distributed out away from the oil injection points without excessive permeability loss. In this work, we examine the distribution of soybean oil in representative aquifer sediments as non-aqueous phase liquid oil (NAPL oil) or as an oil-in-water emulsion. Laboratory columns packed with sands or clayey sands were flushed with either NAPL oil or a soybean emulsion followed by plain water, while monitoring permeability loss and the final oil residual saturation. NAPL oil can be injected into coarse-grained sands. However NAPL injection into finer grained sediments requires high injection pressures which may not be feasible at some sites. In addition, NAPL injection results in high oil residual saturations and moderate permeability losses. In contrast, properly prepared emulsions can be distributed through sands with varying clay content without excessive pressure buildup, low oil retention and very low to moderate permeability loss. For effective transport, the emulsion must be stable, the oil droplets must be significantly smaller than the mean pore size of the sediment and the oil droplets should have a low to moderate tendency to stick to each other and the aquifer sediments. In our work, oil retention and associated permeability loss increased with sediment clay content and with the ratio of droplet size to pore size. For sandy sediments, the permeability loss is modest (0-40% loss) and is proportional to the oil residual saturation.